The overall objective of our proposal is to develop with the aid of computers methods for improving the cost effectiveness and accuracy of diagnostic evaluations of patients with arthritic disorders. More specifically, we propose to: 1) expand our currently existing large database of rheumatic disease patients to include the cost of laboratory tests and procedures both in expense and risk to the patient; 2) generate algorithms to be used to improve diagnostic accuracy; and 3) develop a model for recommending the most cost effective laboratory tests and procedures for the determination of a final rheumatic disease diagnosis. The method that we propose to use is based upon pattern recognition techniques using multi-membership classification methods which allows for simultaneous existence of several disorders in any given patient. We first propose to identify the significance of parameters or features for each possible disorder, estimate the feature's conditional distributions and the disorders prior probabilities including interrelationship such as correlations or dependency among features. Secondly, diagnosis software will then be developed by generating software modules of feature input, posterior probability computation, and an algorithm for feature selection. Thirdly, following the development of the diagnosis software, its accuracy will be evaluated and tested on a separate existing large rheumatic disease data base as well as on individual new cases presenting to the UCLA Arthritis Clinic. Finally, our diagnostic system will be evaluated as to its ability to improve the cost effectiveness of medical care of patients with arthritis. The expense and risk to the patient of the path recommended for diagnosis with the aid of the computer will be compared with the judgment of physicians of various levels of training. This proposal would greatly enhance physician utilization of computer information systems by amplifying the clinical usefulness of data base systems.